Korarchaeum cryptofilum
Introduction
Candidatus Korarchaeum cryptofilum is a species of the proposed phylum Korarchaeota, or Xenarchaeota of the Archaea. The Archean is found mainly in hydrothermal environments such as hot springs, shallow water, and deep ocean. The organism's genome has mainly been studied to open understanding of Archaean evolution.
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Genetics and Structure
The genome of Ca. K. cryptofilum is 1,590,757 base pairs long with an average G+C content of 49%. The arCOG of the archaean phyla crenarchaeota and euryarchaeota when compared with the sequenced genome of Ca. K. cryptofilum, showed that the organism shares its replication, recombination, repair, and cell division genes with that of crenarchaeota while it shares most of its transcription and translation genes with euryarcahaeota.
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Ca. K. cryptofilum has three rRNA genes (16S, 23S, and 5S rRNA. The organism has shown to produce 33 23S r-proteins and 27 16S r-proteins on its rRNA operon. The species is also found to have 45 tRNA genes on its genome with one initiator and 45 elongator tRNAs.[2]
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References
- ↑ > Miller-Coleman, Robin L et al. “Korarchaeota diversity, biogeography, and abundance in Yellowstone and Great Basin hot springs and ecological niche modeling based on machine learning.” PloS one vol. 7,5 (2012): e35964. doi:10.1371/journal.pone.0035964
- ↑ > Miller-Coleman, Robin L et al. “Korarchaeota diversity, biogeography, and abundance in Yellowstone and Great Basin hot springs and ecological niche modeling based on machine learning.” PloS one vol. 7,5 (2012): e35964. doi:10.1371/journal.pone.0035964
- ↑ Hodgkin, J. and Partridge, F.A. "Caenorhabditis elegans meets microsporidia: the nematode killers from Paris." 2008. PLoS Biology 6:2634-2637.
- ↑ Bartlett et al.: Oncolytic viruses as therapeutic cancer vaccines. Molecular Cancer 2013 12:103.
Edited by [Charlie Stutz], student of Joan Slonczewski for BIOL 116 Information in Living Systems, 2019, Kenyon College.
Miller-Coleman, Robin L et al. “Korarchaeota diversity, biogeography, and abundance in Yellowstone and Great Basin hot springs and ecological niche modeling based on machine learning.” PloS one vol. 7,5 (2012): e35964. doi:10.1371/journal.pone.0035964
